While the BAMDP formalism provides a precise recipe to derive the Bayes-optimal policy, finding an analytical expression of this policy is typically not possible. Recent work on meta-reinforcement learning, however, has shown that it is possible to learn an approximation to the Bayes-optimal policy [1]}, [2]}, [3]}. [4]} refer to this approach as RL\(^2\)  because it uses a traditional reinforcement learning algorithm to meta-learn another free-standing reinforcement learning algorithm.
